Image segmentation is a branch of digital image processing that performs the task of categorizing, or classifying, the elements of a digital image into one or more class types. For medical imaging applications, it is common that image segmentation is performed on the voxel (volume element) of a 3-dimensional image data set with the classification types related to anatomical structure. In thoracic medical images, it is convenient to segment the image voxels into classes such as bone, lung parenchyma, soft tissue, bronchial vessels, blood vessels, etc. There are many reasons to perform such a task, such as surgical planning, treatment progress, and patient diagnosis.
Of interest is the image segmentation technology that allows a user of a Picture Archiving and Communications System (PACS) to segment a suspected cancerous lesion. Starting with a seed point, i.e., a voxel position that is known to be part of a lesion, a region of contiguous voxels is grown, or developed, about the seed point. For such lesion segmentation algorithms, the only voxel value know for certainty that is characteristic of the lesion to be segmented is the seed point voxel. Thus, the statistical properties of the voxels associated with the lesion to be segmented, such as the mean voxel value and the range of voxel values, must either be assumed a priori from experience or approximated. Typical algorithmic approaches approximate these statistical quantities by sampling the voxel values within a 2-dimensional or 3-dimensional region about the selected seed point.
Often the statistical approximations made for a given lesion segmentation application are specific to the intended type of lesion tissue being segmented. For example, for pulmonary lesions the mean voxel value can be approximated by the value of the seed voxel, and the range of voxel values can be approximated from experience as ranging from approximately −400 Hounsfield Units (HU) and above. For liver lesion segmentation, however, these statistical quantities may not be useful. What is needed is a statistical sampling algorithm that can approximate the statistical properties of the lesion without regard to lesion type.